SoroushVahidi/scidocs huggingface dataset:
Better Summarization Evaluation with Word Embeddings for ROUGE
(2015)

ROUGE is a widely adopted, automatic evaluation measure for text summarization. While it has been shown to correlate well with human judgements, it is biased towards surface lexical similarities. This makes it unsuitable for the evaluation of abstractive summarization, or summaries with substantial paraphrasing. We study the effectiveness of word embeddings to overcome this disadvantage of ROUGE. Specifically, instead of measuring lexical overlaps, word embeddings are used to compute the semantic similarity of the words used in summaries instead. Our experimental results show that our proposal is able to achieve better correlations with human judgements when measured with the Spearman and Kendall rank co-
QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns
(2015)

Given the extremely large pool of events and stories available, media outlets need to focus on a subset of issues and aspects to convey to their audience. Outlets are often accused of exhibiting a systematic bias in this selection process, with different outlets portraying different versions of reality. However, in the absence of objective measures and empirical evidence, the direction and extent of systematicity remains widely disputed. In this paper we propose a framework based on quoting patterns for quantifying and characterizing the degree to which media outlets exhibit systematic bias. We apply this framework to a massive dataset of news articles spanning the six years of Obama's presidency and all of his speeches, and reveal that a systematic pattern does indeed emerge from the outlet's quoting behavior. Moreover, we show that this pattern can be successfully exploited in an unsupervised prediction setting, to determine which new quotes an outlet will select to broadcast. By encoding bias patterns in a low-rank space we provide an analysis of the structure of political media coverage. This reveals a latent media bias space that aligns surprisingly well with political ideology and outlet type. A linguistic analysis exposes striking differences across these latent dimensions, showing how the different types of media outlets portray different realities even when reporting on the same events. For example, outlets mapped to the mainstream conservative side of the latent space focus on quotes that portray a presidential persona disproportionately characterized by negativity.
Greedy Attribute Selection
(1994)

Many real-world domains bless us with a wealth of attributes to use for learning. This blessing is often a curse: most inductive methods generalize worse given too many attributes than if given a good subset of those attributes. We examine this problem for two learning tasks taken from a calendar scheduling domain. We show that ID3/C4.5 generalizes poorly on these tasks if allowed to use all available attributes. We examine five greedy hillclimbing procedures that search for attribute sets that generalize well with ID3/C4.5. Experiments suggest hillclimbing in attribute space can yield substantial improvements in generalization performance. We present a caching scheme that makes attribute hillclimbing more practical computationally. We also compare the results of hillclimbing in attribute space with FOCUS and RELIEF on the two tasks.
RCS Reduction of Patch Array Antenna by Electromagnetic Band-Gap Structure
(2012)

In this letter, electromagnetic band-gap (EBG) structure is used to reduce the radar cross section (RCS) of the patch array antenna. The proposition of this method is based on the high impedance characteristic of the mushroom-like EBG structure. The basic patch array antenna is designed with a central frequency of 5.0 GHz while replacing the substrate of the array with the mushroom-like EBG structure. The frequency band in which RCS of the patch array antenna reduced significantly can be adjusted by parameters of the EBG. The backward RCS of the patch array antenna with EBG can be reduced as much as 10 dB compared to that of the conventional array antenna, and the degradation of the antenna performance is not significant.
A Frequency Selective Radome With Wideband Absorbing Properties
(2012)

A frequency selective radome is presented, acting as a pass band filter at a given frequency band, while behaving as an absorber above the transmission band. The pass band behavior is obtained by a metallic FSS realized through a compact interdigitated Jerusalem cross element characterized by a very large rejection band. The metallic FSS is used as the ground plane of a thin wideband absorber based on resistive high-impedance surfaces within the total reflection band. The outer absorber reduces the signature of the antenna system when the radome is illuminated by out of band signals. The resistive FSS which comprises the absorber is designed so to minimize losses within the transmitting band of the radome. The composite structure is thoroughly analyzed by an efficient equivalent circuit approach and by full-wave numerical simulations.
Design of Polarization Reconfigurable Antenna Using Metasurface
(2014)

A planar polarization-reconfigurable metasurfaced antenna (PRMS) designed using metasurface (MS) is proposed. The PRMS antenna consists of a planar MS placed atop of and in direct contact with a planar slot antenna, both having a circular shape with a diameter of 78 mm (0.9 λ0), making it compact and low profile. By rotating the MS around the center with respect to the slot antenna, the PRMS antenna can be reconfigured to linear polarization, left-hand and right-hand circular polarizations. An equivalent circuit is used to explain the reconfigurability of the antenna. The PRMS antenna is studied and designed to operate at around 3.5 GHz using computer simulation. For verification of simulation results, the PRMS antenna is fabricated and measured. The antenna performance, in terms of polarization reconfigurability, axial-ratio bandwidth, impedance bandwidth, realized boresight gain and radiation pattern, is presented. Results show that the PRMS antenna in circular polarizations achieves an operating bandwidth of 3.3-3.7 GHz (i.e., fractional bandwidth 11.4%), a boresight gain of above 5 dBi and high-polarization isolation of larger than 15 dB. While the PRMS antenna in linear polarization achieves a gain of above 7.5 dBi with cross-polarization isolation larger than 50 dB.
Understanding of DC leakage and faults in floating photovoltaic systems: Trojan horse to undermine metallic infrastructure and safety
(2017)

This article elaborates on a recently published issue that calls the entire PV community's and utility management attention. It specifically provides further analysis on DC leakage and fault detection blind spots associated with the operation of floating PV Systems. Floating systems are almost a standard practice in Europe when it comes to the operation of large-scale PV parks. A floating PV system has neither the positive nor the negative DC current-carrying conductors connected to earth. The system nevertheless benefits from other earthing connections, including the bonding to earth of exposed metals (e.g. metal frames of the PV modules, supporting infrastructure, combiner boxes) that could become energised in a fault situation or even under normal operation due to DC coupling mechanisms. These may entail safety as well as accelerated DC corrosion concerns.
Combi-Tor: Track-to-Track Association Framework for Automotive Sensor Fusion
(2015)

The data association algorithm plays the vital role of forming an appropriate and valid set of tracks from the available tracks at the fusion center, which are delivered by different sensor's local tracking systems. The architecture of the data association module has to be designed taking into account the fusion strategy of the sensor fusion system, the granularity and the quality of the data provided by the sensors. The current generation environment perception sensors used for automotive sensor fusion are capable of providing estimated kinematic and as well as non-kinematic information on the observed targets. This paper focuses on integrating the kinematic and non-kinematic information in a track-to-track association (T2TA) procedure. A scalable framework called Combi-Tor is introduced here that is designed to calculate the association decision using likelihood ratio tests based on the available kinematic and non-kinematic information on the targets, which are tracked and classified by different sensors. The calculation of the association decision includes the uncertainty in the sensor's local tracking and classification modules. The required sufficient statistical derivations are discussed. The performance of this T2TA framework and the traditional T2TA scheme considering only the kinematic information are evaluated using Monte-Carlo simulation. The initial results obtained using the real world sensor data is presented.
X-Ku Wide-Bandwidth GaN HEMT MMIC Amplifier with Small Deviation of Output Power and PAE
(2014)

A new design methodology was proposed to obtain wide-bandwidth and flat-group-delay reactive-matching GaN HEMT MMIC amplifiers. Frequency dependence of the optimal source and load impedance of a GaN HEMT are derived as polynomial equations and matching circuits are designed by small signal simulation without the use of large-signal transistor model and large-signal simulation. Fabricated GaN HEMT MMIC amplifiers, which show a small deviation of Pout and PAE in the range of 8-18 GHz, prove that our methodology is suitable for the design of a wide-bandwidth MMIC amplifier.
Capacity Analysis of Cooperative Relaying Systems Using Non-Orthogonal Multiple Access
(2015)

In this letter, we propose the cooperative relaying system using non-orthogonal multiple access (NOMA) to improve the spectral efficiency. The achievable average rate of the proposed system is analyzed for independent Rayleigh fading channels, and also its asymptotic expression is provided. In addition, a suboptimal power allocation scheme for NOMA used at the source is proposed.
Tensor Decompositions, State of the Art and Applications
(2008)

In this paper, we present a partial survey of the tools borrowed from tensor algebra, which have been utilized recently in Statistics and Signal Processing. It is shown why the decompositions well known in linear algebra can hardly be extended to tensors. The concept of rank is itself difficult to define, and its calculation raises difficulties. Numerical algorithms have nevertheless been developed, and some are reported here, but their limitations are emphasized. These reports hopefully open research perspectives for enterprising readers. in Mathematics in Signal Processing V, J. G. McWhirter and I. K. Proudler Eds., Oxford University Press, Oxford, UK, 2001
Twin Clouds: An Architecture for Secure Cloud Computing (Extended Abstract)
(2011)

Cloud computing promises a more cost effective enabling technology to outsource storage and computations. Existing approaches for secure outsourcing of data and arbitrary computations are either based on a single tamper-proof hardware, or based on recently proposed fully homomorphic encryption. The hardware based solutions are not scaleable, and fully homomorphic encryption is currently only of theoretical interest and very inefficient. In this paper we propose an architecture for secure outsourcing of data and arbitrary computations to an untrusted commodity cloud. In our approach, the user communicates with a trusted cloud (either a private cloud or built from multiple secure hardware modules) which encrypts and verifies the data stored and operations performed in the untrusted commodity cloud. We split the computations such that the trusted cloud is mostly used for security-critical operations in the less time-critical setup phase, whereas queries to the outsourced data are processed in parallel by the fast commodity cloud on encrypted data.
Etiology of the Psychopathic Serial Killer : An Analysis of Antisocial Personality Disorder , Psychopathy , and Serial Killer Personality and Crime Scene Characteristics
(2007)

The purpose of this article is tomake the distinction between antisocial personality disorder and psychopathy, discuss possible etiologies of psychopathy, and analyze the crimes, personality characteristics, and historical aspects of psychopathic serial killers. The research indicates that both environmental and biological factors affect the development of psychopathy. Several different serial killers were compared to assess the similarities and differences between their histories, crimes, and personalities. Though there were marked differences between their crimes, startling historical and personality similarities were clearly identified. Based on these findings, the validity and reliability of offender profiling is also discussed. [Brief Treatment and Crisis Intervention 7:151–160 (2007)]
May I help you? - Design of Human-like Polite Approaching Behavior-
(2015)

When should service staff initiate interaction with a visitor? Neither simply-proactive (e.g. talk to everyone in a sight) nor passive (e.g. wait until being talked to) strategies are desired. This paper reports our modeling of polite approaching behavior. In a shopping mall, there are service staff members who politely approach visitors who need help. Our analysis revealed that staff members are sensitive to "intentions" of nearby visitors. That is, when a visitor intends to talk to a staff member and starts to approach, the staff member also walks a few steps toward the visitors in advance to being talked. Further, even when not being approached, staff members exhibit "availability" behavior in the case that a visitor's intention seems uncertain. We modeled these behaviors that are adaptive to pedestrians' intentions, occurred prior to initiation of conversation. The model was implemented into a robot and tested in a real shopping mall. The experiment confirmed that the proposed method is less intrusive to pedestrians, and that our robot successfully initiated interaction with pedestrians.
A Review on Deep Learning Techniques Applied to Semantic Segmentation
(2017)

Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we describe the terminology of this field as well as mandatory background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and their targets. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.
A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications
(2018)

Cloud robotics has recently emerged as a collaborative technology between cloud computing and service robotics enabled through progress in wireless networking, large scale storage and communication technologies, and the ubiquitous presence of Internet resources over recent years. Cloud computing empowers robots by offering them faster and more powerful computational capabilities through massively parallel computation and higher data storage facilities. It also offers access to open-source, big datasets and software, cooperative learning capabilities through knowledge sharing, and human knowledge through crowdsourcing. The recent progress in cloud robotics has led to active research in this area spanning from the development of cloud robotics architectures to its varied applications in different domains. In this survey paper, we review the recent works in the area of cloud robotics technologies as well as its applications. We draw insights about the current trends in cloud robotics and discuss the challenges and limitations in the current literature, open research questions and future research directions.
Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks
(2015)

Automatic localization of the standard plane containing complicated anatomical structures in ultrasound (US) videos remains a challenging problem. In this paper, we present a learning-based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN). Compared with previous works based on low-level features, our approach is able to represent the complicated appearance of the FASP and hence achieve better classification performance. More importantly, in order to reduce the overfitting problem caused by the small amount of training samples, we propose a transfer learning strategy, which transfers the knowledge in the low layers of a base CNN trained from a large database of natural images to our task-specific CNN. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method for the FASP localization as well as the CNN only trained on the limited US training samples. The proposed approach can be easily extended to other similar medical image computing problems, which often suffer from the insufficient training samples when exploiting the deep CNN to represent high-level features.
Alternative Splicing: New Insights from Global Analyses
(2006)

Recent analyses of sequence and microarray data have suggested that alternative splicing plays a major role in the generation of proteomic and functional diversity in metazoan organisms. Efforts are now being directed at establishing the full repertoire of functionally relevant transcript variants generated by alternative splicing, the specific roles of such variants in normal and disease physiology, and how alternative splicing is coordinated on a global level to achieve cell- and tissue-specific functions. Recent progress in these areas is summarized in this review.
Survey of STT-MRAM Cell Design Strategies: Taxonomy and Sense Amplifier Tradeoffs for Resiliency
(2017)

Spin-Transfer Torque Random Access Memory (STT-MRAM) has been explored as a post-CMOS technology for embedded and data storage applications seeking non-volatility, near-zero standby energy, and high density. Towards attaining these objectives for practical implementations, various techniques to mitigate the specific reliability challenges associated with STT-MRAM elements are surveyed, classified, and assessed in this article. Cost and suitability metrics assessed include the area of nanomagmetic and CMOS components per bit, access time and complexity, sense margin, and energy or power consumption costs versus resiliency benefits. Solutions to the reliability issues identified are addressed within a taxonomy created to categorize the current and future approaches to reliable STT-MRAM designs. A variety of destructive and non-destructive sensing schemes are assessed for process variation tolerance, read disturbance reduction, sense margin, and write polarization asymmetry compensation. The highest resiliency strategies deliver a sensing margin above 300mV while incurring low power and energy consumption on the order of picojoules and microwatts, respectively, and attaining read sense latency of a few nanoseconds down to hundreds of picoseconds for non-destructive and destructive sensing schemes, respectively.
Antropologia de la Informatica Social: Teoria de la Convergencia Tecno-Social
(2017)

The traditional humanism of the twentieth century, inspired by the culture of the book, systematically distanced itself from the new society of digital information; the Internet and tools of information processing revolutionized the world, society during this period developed certain adaptive characteristics based on coexistence (Human Machine), this transformation sets based on the impact of three technology segments: devices, applications and infrastructure of social communication, which are involved in various physical, behavioural and cognitive changes of the human being; and the emergence of new models of influence and social control through the new ubiquitous communication; however in this new process of conviviality new models like the "collaborative thinking" and "InfoSharing" develop; managing social information under three Human ontological dimensions (h) Information (i) Machine (m), which is the basis of a new physical-cyber ecosystem, where they coexist and develop new social units called "virtual communities ". This new communication infrastructure and social management of information given discovered areas of vulnerability "social perspective of risk", impacting all social units through massive impact vector (i); The virtual environment "H + i + M"; and its components, as well as the life cycle management of social information allows us to understand the path of integration "Techno Social" and setting a new contribution to cybernetics, within the convergence of technology with society and the new challenges of coexistence, aimed at a new holistic and not pragmatic vision, as the human component (h) in the virtual environment is the precursor of the future and needs to be studied not as an application, but as the hub of a new society.
Sentiment-enhanced multidimensional analysis of online social networks: Perception of the mediterranean refugees crisis
(2016)

We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as a case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched with space information (user and mentioned locations), and sentiment (positive vs. negative) w.r.t. refugees. Our study shows differences in positive and negative sentiment in EU countries, in particular in UK, and by matching events, locations and perception, it underlines opinion dynamics and common prejudices regarding the refugees.